title: A simple approach for fitting linear relative rate models in SAS.
abstract: The linear relative rate model has been employed in epidemiologic analyses of a variety of environmental and occupational exposures. In contrast to an exponential rate model, the linear relative rate model implies that the excess relative rate of disease changes in an additive fashion with exposure. The linear relative rate model may be fitted using EPICURE (HiroSoft International Corporation, Seattle, Washington), a specialized statistical software package widely used for such analyses. In this paper, the author presents a simple approach to fitting the linear relative rate model to epidemiologic data using PROC NLMIXED in the SAS statistical software package (SAS Institute Inc., Cary, North Carolina). This approach is illustrated via analyses of data from a study of mortality in a cohort of South Carolina asbestos textile workers (1940-2001).
authors.affiliation: Department of Epidemiology, CB 7435, School of Public Health, University of North Carolina, Chapel Hill, NC 27599, USA. david.richardson@unc.edu
authors.forename: David B
authors.initials: DB
authors.lastname: Richardson
mesh_terms: D000465:Algorithms; D001195:Asbestosis / Q000209:etiology / Q000401:mortality*; D015331:Cohort Studies; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009657:North Carolina / Q000453:epidemiology; D016273:Occupational Exposure / Q000009:adverse effects*; D016012:Poisson Distribution; D012984:Software; D013783:Textile Industry / Q000706:statistics & numerical data*; D013997:Time Factors